In the Wireless telecommunications industry, cellular service providers are intensely interested in providing high quality, reliable services for their customers in today's highly competitive environment. For example, reliability problems such as dropped calls and quality issues such as fading, multi-path interference, and co-channel interference are concerns constantly facing cellular operators. Another issue of great interest to operators is the improvement of perceived speech quality by the end user within the cellular system. Therefore, it is desirable for operators to be able to determine which areas in the network are experiencing quality problems.
There have been a number of methods used in the past to measure speech quality in cellular networks. One commonly used method involves testing a cellular network by transmitting known signals and comparing the received signals to a predefined signal database to determine an estimate for the quality. The term signal is used herein to refer to sounds perceptible in the human audio frequency range which include speech and tones. This method is illustrated in FIG. 1. Depicted is a known signal database 2, wherein predetermined signals are sent through a system under test 4. The system under test 4 represents all the functioning components of a cellular network which includes a mobile switching center (MSC), a radio base station (RBS), all communication links, and the air interface. Once the transmitted signals have been received, a second signal database 6 containing the original signal patterns are compared to the received signals at step 8. An estimate is then calculated for the quality of the received signal for the network.
In digital systems, the conversion of analog speech signals to digital signals requires much more bandwidth for transmission than is desirable. Bandwidth constraints in wireless telecommunication systems have spawned the need for low bit-rate speech coders which work by reducing the number of bits that are necessary to transmit while preserving quality and intelligibility. In general, it is desirable to transmit at lower bit-rates but quality tends to diminish with decreasing bit rates. The speech coders used in these applications work by encoding speech while removing redundancies embedded during speech production.
Typically, speech coders obtain their low bit-rates by modeling human speech production in order to obtain a more efficient representation of the speech signal. The original speech signal can be synthesized using various estimated filter parameters. Since many of the prior art testing methods include the use of audio tones in the testing procedure, they do not lend themselves well for testing with digital systems. This is because, speech coders are modeled after speech production and are not optimized for tones, thus errors in tone regeneration may likely be encountered.
Another source of potential problems with the method of FIG. 1 when utilizing speech signals is in the compare and estimate step 8. Speech database 2 contains a limited number of repeating predetermined sentences (e.g. 6-8 sentences) that are representative of speech patterns typically made through a mobile network. The estimate portion in step 8 employs perceptual models that mimic the listening process. Models; of this type are typically very complicated and difficult to formulate. This leads to differences between the model and the subjective assessment thereby leading to sometimes unreliable measurements.
A predominant factor affecting speech quality in digital systems is the bit error rate (BER). Bit errors tend to be introduced during transmission over the air interface. The BER is the, frequency at which these bit errors are introduced into the transmitted frames. High BER situations often occur during conditions of high co-channel interference, weak signals such as mobile roaming out of range, and fading caused by multi-path interference due to obstructions such as buildings etc. Although attempts are made at correcting these errors, an excessively high BER has a detrimental effect on speech quality.
In a Global System for Mobile Communication (GSM) network for example, the BER and other related parameters, such as Receive Quality (RxQual) and Receive Level (RxLev), are monitored to assess speech quality. There are shortcomings in using this method since correlation relationships and temporal information that can be obtained from the parameters are not taken advantage of to obtain parameters that are more closely related to the speech quality. For example, the extraction of temporal information permits the formulation of a host of relationships between the variables that can be taken advantage of for measuring speech quality. It is known that the perceived speech quality for the end user is associated with time averaging over a length of a sentence at its highest resolution. The final quality is averaged over the whole conversation meaning that the lowest resolution is approximately in the range of several minutes. Therefore the use of derived temporal and correlated parameters, which is lacking in GSM, will give clearer insight as to the state of speech quality experienced for many situations.
The RxQual parameter in the GSM system is measured every 0.5 seconds and is inherently dependent on the BER for each 20 millisecond frame. Further, RxQual can fluctuate widely due to fading, noise or interference which can lead to quality measurements that fluctuate much faster than the perceived speech quality. One seemingly obvious solution would be to increase the temporal resolution with a time constant in the area of 2-5 seconds. But it has been found that the relationship between the digital communication link and speech quality is not solely dependent on a time averaged BER.
What is needed is a method that is both simpler and more accurate than using signal databases and takes advantage of correlation relationships and temporal information from radio link parameters. A further objective is to provide an effective method, using available parameters, that allows operators to monitor quality conditions throughout the network.